Evaluation of 3D correspondence methods for model building

• Published in: Information processing in medical imaging : proceedings of the ... conference Vol. 18; p. 63
• Main Authors: Styner, Martin A, Rajamani, Kumar T, Nolte, Lutz-Peter, Zsemlye, Gabriel, Székely, Gábor, Taylor, Chris J, Davies, Rhodri H
• Format: Journal Article
• Language: English
• Published: Germany 01.01.2003
• Subjects: Algorithms; Artificial Intelligence; Computer Simulation; Femur - anatomy & histology; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique
• ISSN: 1011-2499
• DOI: 10.1007/978-3-540-45087-0_6

The correspondence problem is of high relevance in the construction and use of statistical models. Statistical models are used for a variety of medical application, e.g. segmentation, registration and shape analysis. In this paper, we present comparative studies in three anatomical structures of four different correspondence establishing methods. The goal in all of the presented studies is a model-based application. We have analyzed both the direct correspondence via manually selected landmarks as well as the properties of the model implied by the correspondences, in regard to compactness, generalization and specificity. The studied methods include a manually initialized subdivision surface (MSS) method and three automatic methods that optimize the object parameterization: SPHARM, MDL and the covariance determinant (DetCov) method. In all studies, DetCov and MDL showed very similar results. The model properties of DetCov and MDL were better than SPHARM and MSS. The results suggest that for modeling purposes the best of the studied correspondence method are MDL and DetCov.